Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this thesis presents a comprehensive descriptive analytics of Cloud workload and user behaviours, uncovering the cause and energy related implications of Cloud Computing. Furthermore, the characteristics of Cloud workloads and users including latency levels, job heterogeneity, user dynamicity, straggling task behaviours, energy implications of stragglers, job execution and termination patterns and the inherent periodicity among Cloud workload and user behaviours have been empirically presented. Driven by descriptive analytics, a novel user behaviour forecasting framework has been developed, aimed at a tri-fold forecast of user behaviours including the session duration of users, anticipated number of submissions and the arrival trend of the incoming workloads. Furthermore, a novel resource optimisation framework has been proposed to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness. Both the proposed frameworks have been verified theoretically and tested experimentally based on Google Cloud trace logs. Experimental analysis demonstrates the effectiveness of the proposed framework in terms of the achieved reliability of the forecast results and in reducing the server energy expenditures spent towards executing jobs at the datacentres.

Background: Spinal cord injury (SCI) is defined as a low incidence, high cost condition, however there is little information in the UK regarding the incidence, prevalence or associated costs of SCI. Additionally there is little evidence identifying outcomes or issues associated with delays in referral, admission or discharge from an SCIC or the impact of delays in provision of resources on reintegration outcomes. Research Aim: This novel study aims to determine factors and timings which may facilitate or limit successful rehabilitation and community reintegration for individuals with SCI. Map timescales and key indicators in the SCI injury and rehabilitation and reintegration pathways. Establish the impact of delays in provision of required resources on reintegration outcomes. Methods: An observational longitudinal study, collecting data regarding individuals from injury to one year post-discharge. Rehabilitation, reintegration and healthcare systems outcome measures to be evaluated were identified and included: community participation, quality of life, residential situation, readmission rate and vocational activity. Results: Delays in accessing services occur for a variety of issues and can have lasting impact. Many issues can affect progress and may have a more profound effect at particular points of the injury and rehabilitation pathway. At one year post discharge some subjects have fallen short of the identified outcomes; potential reasons for this are discussed in addition to issues that may have facilitated improved outcomes in some subjects. Conclusion: Some assumptions in SCI rehabilitation and reintegration have been challenged and some partially or fully supported. Novel findings have been identified in relation to physical social and psychological barriers or facilitators of outcomes following SCI. Potential areas for further research to increase our knowledge of issues for SCI individuals, SCIC services, acute hospital services and community services are identified.

在服務資源需求量會隨時間而改變的情況下，系統的服務資源供給對致力於提供高服務品質的資源提供者來說是一個重要的議題。在服務資源可以迅速的部署和解除的假設下，像是以雲端運算為基礎之服務，本研究提供了系統性的估算服務資源方法，本方法之結構是以模擬為基礎並結合了非監督式學習、顧客到達率之估計以及統計技術。首先，本研究將每一日之顧客到達率進行分群運算並將具有類似顧客到達模式的日期分為一群，且每一群之包含日期具備可解釋之代表性；下一階段使用兩階段式的忙碌因子模型去建立每一群的顧客到達率模型，並估計該群的多區間普瓦松分布來做為系統模擬隨機過程所需之參數；最後應用了等候模型理論去設計系統模擬方法，模擬出顧客在系統中到達並接受服務的隨機過程，其結果包含觀察出顧客在系統中的等待時間和排隊長度以及所需之服務資源，並提供在不同的服務策略情形下之表現。 本研究使用了一個來自電力公司客服中心之進線量資料進行本方法之實驗，展示出如何使用本方法建立一個能滿足服務水準要求的服務資源配置策略，也和該公司過去之配置策略進行比較，並提出實質上如何提升服務品質的配置策略之建議。